Hybrid electric buses fuel consumption prediction based on real-world driving data

Ruixiao Sun, Yuche Chen, Abhishek Dubey, Philip Pugliese

Transportation Research Part D: Transport and Environment 2021

PDF DOI

Abstract

Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. In this study, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

Cite This Paper

@article{SUN2021102637,
  author = {Sun, Ruixiao and Chen, Yuche and Dubey, Abhishek and Pugliese, Philip},
  journal = {Transportation Research Part D: Transport and Environment},
  title = {Hybrid electric buses fuel consumption prediction based on real-world driving data},
  year = {2021},
  issn = {1361-9209},
  pages = {102637},
  volume = {91},
  abstract = {Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. In this study, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2\% for microscopic models and 5–8\% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.},
  contribution = {colab},
  doi = {https://doi.org/10.1016/j.trd.2020.102637},
  keywords = {Hybrid diesel transit bus, Artificial neural network, Fuel consumption prediction},
  tag = {transit},
  url = {https://www.sciencedirect.com/science/article/pii/S1361920920308221}
}
Quick Info
Year 2021
DOI https://doi.org/10.1016/j.t...
Keywords
Hybrid diesel transit bus Artificial neural network Fuel consumption prediction
Search Tags

Hybrid, electric, buses, fuel, consumption, prediction, real, world, driving, data, Hybrid diesel transit bus, Artificial neural network, Fuel consumption prediction, 2021, Sun, Chen, Dubey, Pugliese